A Geographic Exploration of Mental Healthcare

Our Topic

For this project, we chose to continue to explore mental health as we did in our Shiny app. However, while our Shiny app focused exclusively on students, we wanted to expand our target population to include all adults ages 18-39. Additionally, our previous project explored how demographic factors such as age, race, and education affected mental health diagnoses and outcomes; while we are still looking at diagnoses and outcomes here, we wanted to see how incorporating spatial data - specifically state-by-state breakdowns - might lead us to additional insights in these areas.

Our Data

Our data comes from the Substance Abuse and Mental Health Services Administration’s (SAMHSA) Mental Health Client Level Data for 2020. According to SAMHSA, the data “are for individuals receiving mental health treatment services provided or funded through state mental health agencies”. Because of this overall population represented in the dataset, we narrowed it down further to only include those who received at least one primary diagnosis from mental healthcare providers (since it is already an unrepresentative sample in that sense), as well as our age range of 18-39.

Exploring the Data

Mental Health Comorbidities by State

To begin, we created an interactive choropleth exploring how often clients who are diagnosed with one condition are also given additional diagnoses, broken down by US state.

  • Certain states have high comorbidity rates regardless of primary diagnosis (e.g. Minnesota, Washington, Virginia), often approaching 80%; however, these states often have low to average comorbidity rates when the primary diagnosis is delirium/dementia, substance use disorders, or developmental disorders.
  • Some disorders, like depressive, bipolar, and conduct, appear to have higher comorbidity rates nationwide
  • California has a low comorbidity rate across most primary diagnoses even compared to others with similar population. Arkansas’ low comorbidity rate is at an even larger extreme (one of the lowest for every primary diagnosis).
  • No real patterns emerge regionally…but could we explore something else to help explain why certain states have higher or lower overall rates of comorbidity?

Additionally, we created a table (https://vchappell.shinyapps.io/ServicesTable/) observing the different health services used by participants which can be filtered by Gender, Employment Status, and Education level, broken down at a state level.

To help understand this table, the different abbreviated services are as follows :

  • SPHSERVICE -> State-psychiatric hospital
  • CMPSERVICE -> SMHA-funded/operated community-based program
  • OPISERVICE -> Other psychiatric inpatient center
  • RTCSERVICE -> Residential treatment center
  • IJSSERVICE -> Institution under the justice system

The display of this data will eventually be reformatted to display a proportion of the whole such that results are more easily understood, but some preliminary results are:

  • For nearly every state, there is an inverse relationship between those served by residential treatment centers and institutions under the justice system (i.e., if more are served by an IJS, less are served by a RTC and vice-versa.)

  • Community-based program services are the most utilized services in each state compared to the other services documented.

  • In nearly all states, there is a higher sum of men seen in state-psychiatric hospitals, other psychiatric inpatient centers, and institutions under the justice system than women.

Intro

This is an R Markdown blog template. This document will be knit to HTML to produce a webpage that will be hosted publicly via GitHub.

Website publication work flow

  1. Edit Rmd

  2. Knit to HTML to view progress. You may need to click “Open in Browser” for some content to show (sometimes content won’t show until you actually push your changes to GitHub and view the published website).

  3. Commit and push changes when you are ready. The website may take a couple minutes to update automatically after the push, but you may need to clear your browser’s cache or view the page in a private/incognito window to see the changes more quickly.

Content

You can include text, code, and output as usual. Remember to take full advantage of Markdown and follow our Style Guide.

Examples and additional guidance are provided below.

Take note of the the default code chunk options in the setup code chunk. For example, unlike the rest of the Rmd files we worked in this semester, the default code chunk option is echo = FALSE, so you will need to set echo = TRUE for any code chunks you would like to display in the blog. You should be thoughtful and intentional about the code you choose to display.

Visualizations

Visualizations, particularly interactive ones, will be well-received. That said, do not overuse visualizations. You may be better off with one complicated but well-crafted visualization as opposed to many quick-and-dirty plots. Any plots should be well-thought-out, properly labeled, informative, and visually appealing.

If you want to include dynamic visualizations or tables, you should explore your options from packages that are built from htmlwidgets. These htmlwidgets-based packages offer ways to build lighterweight, dynamic visualizations or tables that don’t require an R server to run! A more complete list of packages is available on the linked website, but a short list includes:

  • plotly: Interactive graphics with D3
  • leaflet: Interactive maps with OpenStreetMap
  • dygraphs: Interactive time series visualization
  • visNetwork: Network graph visualization vis.js
  • sparkline: Small inline charts
  • threejs: Interactive 3D graphics

You may embed a published Shiny app in your blog if useful, but be aware that there is a limited window size for embedded objects, which tends to makes the user experience of the app worse relative to a dedicated Shiny app page. Additionally, Shiny apps will go idle after a few minutes and have to be reloaded by the user, which may also affect the user experience.

Any Shiny apps embedded in your blog should be accompanied by the link to the published Shiny app (I did this using a figure caption in the code chunk below, but you don’t have to incorporate the link in this way).

Tables

DT package

The DT package is great for making dynamic tables that can be displayed, searched, and filtered by the user without needing an R server or Shiny app!

Note: you should load any packages you use in the setup code chunk as usual. The library() functions are shown below just for demonstration.

library(DT)
mtcars %>% 
  select(mpg, cyl, hp) %>% 
  datatable(colnames = c("MPG", "Number of cylinders", "Horsepower"),
            filter = 'top',
            options = list(pageLength = 10, autoWidth = TRUE))

kableExtra package

You can also use kableExtra for customizing HTML tables.

library(kableExtra)
summary(cars) %>%
  kbl(col.names = c("Speed", "Distance"),
      row.names = FALSE) %>%
  kable_styling(bootstrap_options = "striped",
                full_width = FALSE) %>%
  row_spec(0, bold = TRUE) %>%
  column_spec(1:2, width = "1.5in") 
Speed Distance
Min. : 4.0 Min. : 2.00
1st Qu.:12.0 1st Qu.: 26.00
Median :15.0 Median : 36.00
Mean :15.4 Mean : 42.98
3rd Qu.:19.0 3rd Qu.: 56.00
Max. :25.0 Max. :120.00

Images

Images and gifs can be displayed using code chunks:

"Safe Space" by artist Kenesha Sneed

“Safe Space” by artist Kenesha Sneed

This is a figure caption

This is a figure caption

You may also use Markdown syntax for displaying images as shown below, but code chunks offer easier customization of the image size and alignment.

This is another figure caption

Either way, the file path can be a local path within your project directory or a URL for an image hosted online. This syntax works for PNG, PDF, JPG, and even GIF formats.

Videos

You can use code chunks or Markdown syntax include links to any valid YouTube or Vimeo URLs (see here for details) or point to a location within your project directory.

Code chunk:

Markdown syntax:

You may need to push your updates to GitHub to see if the videos work.

Equations

You might include equations if part of the purpose of your blog is to explain a statistical method. There are two ways to include equations:

  • Inline: \(b \sim N(0, \sigma^2_b)\)
  • Display-style (displayed on its own line): \[\frac{\sigma^2_b}{\sigma^2_b + \sigma^2_e}\]

For typesetting equations appropriately, check out the AMS-LaTeX quick reference or take a look at the Symbols in math mode section of this cheat sheet (or do some extra Googling—there are many resources).

Formatting

Tabbed subsections

Each subsection below the “Tabbed subsections” section heading will appear in a tab. See R Markdown Cookbook Section 7.6: Put content in tabs for additional customization options.

Bulleted list

You can make a bulleted list like this:

  • item 1
  • item 2
  • item 3

Numbered list

You can make a numbered list like this

  1. First thing I want to say
  2. Second thing I want to say
  3. Third thing I want to say

Column formatting

Content Column 1

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse vel ipsum eu sem facilisis porttitor. Integer eu tristique lectus. Vestibulum nisi risus, porta sit amet cursus nec, auctor ac tellus. Integer egestas viverra rhoncus. Fusce id sem non ante vestibulum posuere ac sed lorem. Proin id felis a mi pellentesque viverra in at nulla. Duis augue nulla, aliquet ac ligula a, sagittis varius lorem.

Content Column 2

Aliquam non ante et erat luctus hendrerit eu ac justo. Fusce lacinia pulvinar neque non laoreet. Fusce vitae mauris pharetra, scelerisque purus eget, pharetra nisl. Aenean volutpat elementum tortor vitae rhoncus. Phasellus nec tellus euismod neque congue imperdiet tincidunt in mauris. Morbi eu lorem molestie, hendrerit lorem nec, semper massa. Sed vulputate hendrerit ex, eget cursus purus. Pellentesque consequat erat leo, eleifend porttitor lacus porta at. Vivamus faucibus quam ipsum, id condimentum ligula malesuada ultrices. Nullam luctus leo elit, vitae rutrum nibh venenatis eget. Nam at sodales purus. Proin nulla tellus, lacinia eget pretium sed, vehicula aliquet neque. Morbi vel eros elementum, suscipit elit eu, consequat libero. Nulla nec aliquet neque. Nunc bibendum sapien lectus, sed elementum nisi rutrum non. Ut vulputate at lacus eget maximus.

Customizing your blog design

As a final detail only if you have time, you can explore options for customizing the style of your blog. By default, we are using the readthedown theme from the rmdformats package (see Line 6 of this file if you want to switch out themes).

Theme

You can use the rmdformats package to play around with some pre-built themes. There are, I’m sure, many many many more similar packages with built in themes, or you can look into how to include a CSS code chunk to customize aspects of a theme.

Using the rmdformats package, you can change the theme itself (Line 6):

  • rmdformats::readthedown
  • rmdformats::downcute
    • For downcute only, you can add a new indented line below Line 6 with the code downcute_theme: "chaos" for the downcute chaos theme
  • rmdformats::robobook
  • rmdformats::material

You can explore additional YAML options by looking at the rmdformats package page or running, for example, ?rmdformats::readthedown() to see the help documentation for a particular theme from the package.

Synax highlighting

You can also change the code chunk syntax highlighting option (Line 7, highlight):

  • "default"
  • "tango"
  • "pygments"
  • "kate"
  • "monochrome"
  • "espresso"
  • "zenburn"
  • "haddock"
  • "textmate"
  • NULL for no syntax highlighting (not recommended)

Font size, type, and other customization

Further customization requires adding a CSS style file or code chunk or incorporating other development options. Customization beyond the rmdformats package should be your lowest and final priority for the project. Ensure your content is fully prepared first.

References

All data sources, any key R packages, and any other sources used in developing your blog should be cited in full in a list of references at the end of your blog. Your blog post should also link to these sources as they are discussed. You may choose any reference style as long as sources are fully cited (try to be consistent!).

Typically, references in R Markdown (and LaTeX) files are incorporated with a BibTeX database (a .bib file). You can try this approach or manually include either a numbered or alphabetized list.

Columbia University has compiled some guidance on how to cite data. Some data sources will give you the citation information to copy and paste. Use the provided citations or citation styles in those cases.

You can list R package citations with the code citation("packageName") in the console and then copy (and reformat as needed) the relevant text, e.g.,

## 
## To cite package 'DT' in publications use:
## 
##   Xie Y, Cheng J, Tan X (2023). _DT: A Wrapper of the JavaScript
##   Library 'DataTables'_. R package version 0.27,
##   <https://CRAN.R-project.org/package=DT>.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {DT: A Wrapper of the JavaScript Library 'DataTables'},
##     author = {Yihui Xie and Joe Cheng and Xianying Tan},
##     year = {2023},
##     note = {R package version 0.27},
##     url = {https://CRAN.R-project.org/package=DT},
##   }

The following citations are based on the American Statistical Association citation style (not all of these references are used in this document).:

Baumer, B. S., Kaplan, D. T., and Horton, N. J. (2021), Modern Data Science with R (2nd ed.), Boca Raton, FL: CRC Press.

Broman, K. W. and Woo, K. H. (2018), “Data Organization in Spreadsheets,” The American Statistician, 72:1, 2-10, doi: 10.1080/00031305.2017.1375989

Columbia University Libraries (n.d.), “Data Citation,” available at https://guides.library.columbia.edu/datacitation.

McNamara, A. and Horton N. J. (2018) “Wrangling Categorical Data in R,” The American Statistician, 72:1, 97-104, doi: 10.1080/00031305.2017.1356375.

Shah, Syed A. A. (October 2022), “Starbucks Drinks” (Version 1), Kaggle, available at https://www.kaggle.com/datasets/syedasimalishah/starbucks-drinks.

Xie Y, Cheng J, Tan X (2022). “DT: A Wrapper of the JavaScript Library ‘DataTables’,” R package version 0.24, available at https://CRAN.R-project.org/package=DT.